SynthesisFrontiers in oncology2022
Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis.
Synthesis in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 7 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Pooled it
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- 18F-FDG PET/CT Semiquantitative and Radiomic Features for Assessing Pathologic Axillary Lymph Node Status in Clinical Stage I-III Breast Cancer Patients: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis.PloS one · 2024Pooled it
- MRI-based radiomics with SHAP interpretation for preoperative prediction of upstaging in ductal carcinomaTranslational cancer research · 2026Article
- Mammography-based artificial intelligence model for predicting axillary lymph node status after neoadjuvant therapy in breast cancer.European radiology · 2026Article
- Assessing the performance of deep learning and hand-crafted radiomics models using MRI and ultrasound in predicting axillary lymph node status in breast cancer: a systematic review and meta-analysis.European radiology · 2026Article
- ECG-derived Poincaré plot graphical metrics for differentiating axillary lymph node metastasis in breast cancer.Frontiers in physiology · 2026Article
- Multi-parametric MRI habitat radiomics with interpretable machine learning for early prediction of axillary lymph node metastasis in triple-negative breast cancer.Frontiers in medicine · 2026Article
- Development and External Validation of a Preoperative Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer Using Routine Ultrasound and Core Needle Biopsy Variables.Cancer management and research · 2026Article
- Radiomics integrated with machine and deep learning analysis of T2-weighted and arterial-phase T1-weighted Magnetic Resonance Imaging for non-invasive detection of metastatic axillary lymph nodes in breast cancer.La Radiologia medica · 2025Article
- Development of a Machine Learning Model Integrating Pathomics and Clinical Data to Predict Axillary Lymph Node Metastasis in Breast Cancer: A Two-Center Study.Cancer reports (Hoboken, N.J.) · 2025Article
- Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis.BMC cancer · 2025Article
- Predictive modeling for metastasis in oncology: current methods and future directions.Annals of medicine and surgery (2012) · 2025Review
- A deep learning model based on chest CT to predict benign and malignant breast masses and axillary lymph node metastasis.Biomolecules & biomedicine · 2025Article
- Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer. Materials and methods: Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity. Results: A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis ( Conclusion: Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.
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